Development and Validation of a Mobile Application to Estimate Jump Height from Audio Recordings
Mobile applications; exercise test; functional physical fitness
Vertical jump assessment is crucial in the field of sports and physical conditioning, as it allows the evaluation of parameters such as lower limb power, training effects, and muscle fatigue. Mobile apps can accurately and reliably measure jump height from slow-motion captured videos. However, these apps require a manual identification of the take-off and landing frames, which can be time-consuming and susceptible to human error. This study aimed to develop a novel method for automatically estimating jump height from the sound produced, using audio processing techniques. The concurrent validity of the audio method (A) was investigated compared to jump height estimates from a force platform, applying the flight time (FT) and impulse-momentum (J) methods, considered the "gold standard". Fifty participants (26 ± 9 years) jumped onto a force platform (criterion method) while a mobile phone recorded the jump sound. A custom interface was developed for collecting and processing force platform signals. Validity was determined by regression analysis, Pearson correlation coefficient (r), and standard error of estimate (SEE). Near-perfect correlations were found between the audio and FT (r = 0.99, SEE = 1.4 cm) and J (r = 0.96, SEE = 2.2 cm) methods. The results show that jump height can be measured accurately and quickly from an audio recording using an automated mobile app. This method was implemented in a mobile app (Jumpo 2), made freely available on Android and IOS platforms.